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Comparative Analysis on Blood Cell Image Segmentation
Journal
Proceedings Of The 2nd International Symposium On Computer, Communication, Control And Automation
Date Issued
2013
Author(s)
Muda, TZT
Salam, RA
Abstract
Image segmentation is an important phase in image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present a comparative analysis on several segmentation algorithms. Three selected common approaches, that are Fuzzy c-means, K-means and Mean-shift were presented. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method that is K-means was selected. K-means has been enhanced by integrating Median-cut algorithm to further improve the segmentation process. The proposed integrated method has shown a significant improvement in the number of selected regions.